New Scientist Live

A thinking man

THIS is the best book on computers I have ever read. And I write that as
someone who has struggled through interminable computer science textbooks in the
search for something that students might find even remotely engaging,
interesting or at least small enough not to need a small backpack to carry.

After all that, Daniel Hillis’s The Pattern on the Stone is a breath
of fresh air. Hillis knows what he is talking about: he began his career at MIT.
He became co-founder and chief scientist of Thinking Machines (makers of the
Connection Machine, one of the first and speediest parallel processing
computers) and most recently vice-president and Disney Fellow at Walt Disney
Imagineering.

But for me Hillis’s most impressive achievement has to be that, as a small
child, he built a mechanical man out of motors and lights, without seriously
injuring himself. His lifelong interest in computers started with the
realisation that what was missing from his robot was the ability to think or
compute.

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Recalling the standpoint of a small child captivated by possibilities,
complexities and challenges of building a fully-functioning robot, Hillis has
written “the book I wish I had read when I first started learning about the
field of computing”.

In 150 or so short pages, Hillis takes us on a lightning tour of the
fundamentals of computing, beginning with Boolean logic, finite-state machines
and programming languages, through functional abstraction and recursion, to
Turing machines, quantum computing, public-key encryption, parallel computing,
neural networks and the simulated evolution of intelligence. Nowhere does Hillis
lose sight of the fact that what is important is not the detail of these issues,
but the story that flows through them and the rationality of thought that
connects them.

His other achievement is not to pretend that he is setting down the “truth”
about his field. Scattered throughout are Hillis’s reflections, opinions and
statements of personal interest. When discussing parallel architectures, for
example, you’re getting the story straight from the source. Hillis arrived at
MIT’s artificial intelligence laboratory as an undergraduate in 1974 when
the field was in a stage of explosive growth.

“The first programs that could follow simple instructions in plain English
were being developed, and a computer that understood human speech seemed just
around the corner,” he recalls. Several years later, Hillis rejoined the lab as
a graduate student at a time when the intractability of some AI problems was
becoming apparent. As he explains: “Although lots of new principles and powerful
methods had been invented, applying them to larger, more complicated problems
didn’t seem to work.” He was one of the first advocates of the parallel computer
architecture that solved a few of those problems in the late 1970s.

Parallel computers, argues Hillis, are a powerful solution to the inherent
limitations of standard, serial computer architecture, where one thing is done
at a time like a production line, with each worker performing a specific task.
Parallel computers, by contrast, do many things at once, like a team of people
working on one task together, and so have the potential to be very fast indeed.
But the idea of using computers in parallel—linking many
processors—seemed beset by problems. “I spent a large part of my early
career arguing with people who believed it was impractical, or even impossible,
to build and program general-purpose massively parallel computers,” Hillis
recalls.

One of those people was Gene Amdahl, who in the 1960s proposed the law that
says, essentially, that there will always be a part of any computer operation
which is sequential: that is, it can only be done one step at a time. Even if 90
per cent of a task is possible using a parallel approach, the final 10 per cent
will form a bottleneck. Hillis’s insight was that the sequential part of a task
was much smaller than 10 per cent. By dividing the task between the various
processors in a parallel computer you could avoid the bottleneck altogether.
Hillis says his approach is based on the way the human brain combines many
neurons working in parallel. “Because I knew that the brain was able to get fast
performance from slow components, I also knew that Amdahl’s law does not also
apply”.

Hillis also makes no bones about the fact that he is firmly of the belief
that human intelligence is largely a matter of the computational properties of
our 1011 self-organising neurons and their 1014 connections, even if we don’t
yet really understand the way the brain works in any more than a sketchy
outline. He responds to the critics of the computational view by pointing out
that “Most of us do not appreciate being likened to machines. This is
understandable: we ought to be insulted to be likened to stupid machines, such
as toasters and automobiles, or even today’s computers.” As he says, “The simple
caricature of thought within today’s computers” has much to teach us.

Hillis also does a good job of rehabilitating some of the more extreme
pronouncements of AI’s pioneers, who have predicted the arrival of the
artificially intelligent computer for a long time. Hillis simply suggests that
such an intelligence may be evolved, and that is why such a long time is
required. But he does not merely assert this. He takes the trouble to identify
the mechanism most likely to achieve the required result: evolutionary
development of a variety of intelligences in successively richer simulated
environments, beginning with computers we would recognise and ending with
something intelligent, evolved but not born and beyond our imagination.

The latter will prove a Pandora’s box, Hillis argues. If we assist the
evolution of artificial intelligence, we will usher in problems of selfhood, of
authority and rights. The arrival of an artificially intelligent computer
heralds the arrival of a whole tangle of moral issues to confront. What, he
asks, will we do if, for example, we are faced with the dilemma of pulling the
plug on an artificially intelligent being?

And there’s plenty more to come, much of it beyond our imaginings. The
Internet, for example, is an ocean of e-mail at the moment. What, he wonders,
will happen when the Net connects physical devices such as the computers in
telephone networks to home appliances. For Hillis, the direct communication
between them will provide a rich environment, a source of “emergent behaviour
going beyond any that has been explicitly programmed into the system”.

It’s this mixture of anecdote, insight and challenging ideas that marks this
book out from the herd. The Pattern on the Stone will prove excellent
reading for many an audience, from undergraduates to readers who just want to
know what all the fuss is about. I plan to donate my copy to my 76-year-old
father along with his new Apple Macintosh and first Net account. I hope that he
sends Daniel Hillis an e-mail and tells him he enjoyed the book.

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Peter Thomas is a computer scientist at the University of the West of
England